Indoor concentrations of PM10, PM2.5, CO, and CO2 were measured in 25 naturally ventilated urban residences during the winter and summer seasons in Alexandria, Egypt. Ambient air samples were also collected simultaneously for comparison to indoor measurements. Furthermore, data for air exchange rates, home characteristics, and indoor activities during sampling were collected. It was found that the average indoor PM10, PM2.5, CO, and CO2 concentrations for all homes in winter were 119.4 ± 30.9 μg/m3, 85.2 ± 25.8 μg/m3, 1.6 ± 0.8 ppm, and 692.4 ± 144.6 ppm, respectively. During summer, the average indoor levels were 98.8 ± 21.8 μg/m3, 67.8 ± 14.9 μg/m3, 0.5 ± 0.5 ppm, and 558.2 ± 66.2 ppm, respectively. The results indicate that the indoor daily averages of PM10 and PM2.5 concentrations were higher than the World Health Organization (WHO) guidelines for all selected homes in the two sampling periods. For CO and CO2 levels, the indoor daily averages for all monitored homes were less than the WHO guideline and thAQ standards in Egypt.Implications Personal exposure is considerably influenced by indoor air pollution which increases health risks. Assessment of indoor air quality has become a more significant issue in Egypt as people tend to spend most of their time inside buildings, especially in their homes. Currently, there is a lack of research on residential indoor air quality in Egyptian cities in terms of the spatial and temporal variation which prevents an accurate assessment of the current situation to develop effective mitigation measures and to establish national indoor air quality standards. This article is considered the first research studying the effect of seasonality on indoor concentrations of PM10, PM2.5, CO, and CO2 in urban residences in Alexandria. It also studies the indoor/outdoor relationship of air pollutants’ levels and identifies their major sources as well as the various determinants that influence their indoor concentrations.Several deep learning approaches have been proposed to address the challenges in computational pathology by learning structural details in an unbiased way. Transfer learning allows starting from a learned representation of a pretrained model to be directly used or fine-tuned for a new domain. However, in histopathology, the problem domain is tissue-specific and putting together a labelled data set is challenging. On the other hand, whole slide-level annotations, such as biomarker levels, are much easier to obtain. We compare two pretrained models, one histology-specific and one from ImageNet on various computational pathology tasks. We show that a domain-specific model (HistoNet) contains richer information for biomarker classification, localization of biomarker-relevant morphology within a slide, and the prediction of expert-graded features. We use a weakly supervised approach to discriminate slides based on biomarker level and simultaneously predict which regions contribute to that prediction. We employ multitask learning to show that learned representations correlate with morphological features graded by expert pathologists. All of these results are demonstrated in the context of renal toxicity in a mechanistic study of compound toxicity in rat models. Our results emphasize the importance of histology-specific models and their knowledge representations for solving a wide range of computational pathology tasks.Human epidermal growth factor receptor2 (HER2) and Vascular endothelial growth factor receptor2 (VEGFR2) – a tyrosine kinase receptors play a key role in breast and stomach cancers. The overexpression of HER2 and VEGFR2 genes increases the number of HER2 and VEGFR2 in the cell which initiates breast and stomach cancer respectively. The phytochemicals from traditional medicinal herb Houttuynia cordata Thunb. are reported to possess anti-inflammatory and anti-cancer potential. However, isolation of phytochemicals from this herb is fraught with uncertainly and time-consuming. Here, a molecular docking approach provides probable binding affinities between the receptors and phytochemicals (ligands) which initiate the first step of anticancer drug discovery and development. In the present study, In-silico docking approaches were used to identify the top-hit phytochemicals from H. cordata as potential inhibitors for overexpressed HER2 (breast) and VEGFR2 (stomach) cancer genes. A total of 100 biologically active phytochemicals from H. cordata were screened and docked against the ligand-binding pocket of HER2 and VEGFR2 kinase domains. Docking results revealed only a few phytochemicals (molecules) which appropriately fit into the ligand-binding pocket with higher binding affinity than the natural ATP ligand. A competitive docking was used to ascertain the top-hit phytochemicals that bind perfectly to the ATP ligand-binding pocket. Among the top-hit phytochemicals docked from H. cordata, the β-sitosterol and Quercetin showed highest binding affinity towards HER2 and VEGFR2 receptors using both hydrogen and hydrophobic interactions. This study confirmed β-sitosterol and Quercetin as potential drug candidates against breast and stomach cancer.Communicated by Ramaswamy H. Sarma.In December 2019, a new type of SARS corona virus emerged from China and caused a globally pandemic corona virus disease (COVID-19). This highly infectious virus has been named as SARS-CoV-2 by the International Committee of the Taxonomy of Viruses. It has severely affected a large population and economy worldwide. Globally various scientific communities have been involved in studying this newly emerged virus and is lifecycle. Multiple diverse studies are in progress to design novel therapeutic agents, in which understanding of interactions between the target and drug ligand is a significant key for this challenge. Structures of proteins involved in the life cycle of the virus have been revealed in RCSB PDB by researchers. In this study, we employed molecular docking study of 4-Acetamido-3-nitrobenzoic acid (ANBA) with corona virus proteins (spike protein, spike binding domain with ACE2 receptor and Main protease, RNA-dependent RNA polymerase). Selleckchem Phospho(enol)pyruvic acid monopotassium Single crystal X-ray analysis and density functional theory calculations were carried out for ANBA to explore the structural and chemical-reactive parameters.